{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2. Converting a Jupyter notebook to other formats with nbconvert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = ('https://github.com/ipython-books/'\n",
    "       'cookbook-2nd-data/blob/master/'\n",
    "       'test.ipynb?raw=true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3857\n"
     ]
    }
   ],
   "source": [
    "contents = requests.get(url).text\n",
    "print(len(contents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      " \"cells\": [\n",
      "  {\n",
      "   \"cell_type\": \"markdown\",\n",
      "   \"metadata\": {},\n",
      "   \"source\": [\n",
      "    \"# First chapter\"\n",
      "   ]\n",
      "  },\n",
      "  {\n",
      "   \"cell_type\": \"markdown\",\n",
      "   \"metadata\": {\n",
      "    \"my_field\": [\n",
      "     \"value1\",\n",
      "     \"2405\"\n",
      "    ]\n",
      "   },\n",
      "   \"source\": [\n",
      "    \"Let's write some *rich* **text** with\n",
      "        [links](http://www.ipython.org) and lists:\\n\",\n",
      "    \"\\n\",\n",
      "    \"* item1...rmat\": 4,\n",
      " \"nbformat_minor\": 4\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(contents[:345] + '...' + contents[-33:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "nb = json.loads(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['cells', 'metadata',\n",
      "           'nbformat', 'nbformat_minor'])\n",
      "nbformat 4.4\n"
     ]
    }
   ],
   "source": [
    "print(nb.keys())\n",
    "print('nbformat %d.%d' % (nb['nbformat'],\n",
    "                          nb['nbformat_minor']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cell_type': 'markdown',\n",
       " 'metadata': {'my_field': ['value1', '2405']},\n",
       " 'source': [\"Let's write some *rich* **text** with\n",
       "        [links](http://www.ipython.org) and lists:\\n\",\n",
       "  '\\n',\n",
       "  '* item1\\n',\n",
       "  '* item2\\n',\n",
       "  '    1. subitem\\n',\n",
       "  '    2. subitem\\n',\n",
       "  '* item3']}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb['cells'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cell_type': 'code',\n",
       " 'execution_count': 1,\n",
       " 'metadata': {},\n",
       " 'outputs': [{'data': {'image/png': 'iVBOR...QmCC\\n',\n",
       "    'text/plain': ['<matplotlib Figure at ...>']},\n",
       "   'metadata': {},\n",
       "   'output_type': 'display_data'}],\n",
       " 'source': ['import numpy as np\\n',\n",
       "  'import matplotlib.pyplot as plt\\n',\n",
       "  '%matplotlib inline\\n',\n",
       "  'plt.figure(figsize=(2,2));\\n',\n",
       "  \"plt.imshow(np.random.rand(10,10),\n",
       "              interpolation='none');\\n\",\n",
       "  \"plt.axis('off');\\n\",\n",
       "  'plt.tight_layout();']}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb['cells'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 Markdown cells and 1 code cells.\n"
     ]
    }
   ],
   "source": [
    "cells = nb['cells']\n",
    "nm = len([cell for cell in cells\n",
    "          if cell['cell_type'] == 'markdown'])\n",
    "nc = len([cell for cell in cells\n",
    "          if cell['cell_type'] == 'code'])\n",
    "print((f\"There are {nm} Markdown cells and \"\n",
    "       f\"{nc} code cells.\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image/png': 'iVBOR...QmCC\\n',\n",
       " 'text/plain': ['<matplotlib.figure.Figure at ...>']}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cells[2]['outputs'][0]['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We write the notebook to a file on disk.\n",
    "with open('test.ipynb', 'w') as f:\n",
    "    f.write(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook test.ipynb to html\n",
      "[NbConvertApp] Writing 253784 bytes to test.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to html test.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "podoc": {
     "output_text": "HTML export"
    }
   },
   "outputs": [
    {
     "data": {
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cCWYhJ2/0279tI1H2OKJdt8qeA62u3jgZPJqd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\nrO5NOtJdDxcZm9jsyJ2xfst5xWTy2nKvV5avJZOGWorvUc7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RzuOOiQUXmykuBQBC0L0w3tf9/IUrGc/P21ElMr+3hW+LXLnw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\n8LeIEEIIIYQQQgghlB8MTSGEUMkxJ17+3omU/2HP/fnAEl+KdfNX5qfnEmG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      "text/plain": [
       "HTML export"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('test.html', 600, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing temp.tplx\n"
     ]
    }
   ],
   "source": [
    "%%writefile temp.tplx\n",
    "((*- extends 'article.tplx' -*))\n",
    "\n",
    "((* block author *))\n",
    "\\author{Cyrille Rossant}\n",
    "((* endblock author *))\n",
    "\n",
    "((* block title *))\n",
    "\\title{My document}\n",
    "((* endblock title *))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook test.ipynb to pdf\n",
      "[NbConvertApp] Support files will be in test_files/\n",
      "[NbConvertApp] Making directory test_files\n",
      "[NbConvertApp] Writing 16695 bytes to notebook.tex\n",
      "[NbConvertApp] Building PDF\n",
      "[NbConvertApp] Running xelatex 3 times:\n",
      "    ['xelatex', 'notebook.tex']\n",
      "[NbConvertApp] Running bibtex 1 time:\n",
      "    ['bibtex', 'notebook']\n",
      "[NbConvertApp] PDF successfully created\n",
      "[NbConvertApp] Writing 16147 bytes to test.pdf\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "jupyter nbconvert --to pdf --template temp test.ipynb"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 2
}
